论文标题
SMBOP：半自动回调的自下而上的语义解析
SmBoP: Semi-autoregressive Bottom-up Semantic Parsing
论文作者
论文摘要
近年来，用于语义解析的事实上的标准解码方法是使用自上而下的深度优先遍历自动逐渐解析目标程序的抽象语法树。 In this work, we propose an alternative approach: a Semi-autoregressive Bottom-up Parser (SmBoP) that constructs at decoding step $t$ the top-$K$ sub-trees of height $\leq t$.与自上而下的自动回归解析相比，我们的解析器具有多种好处。从效率的角度来看，自下而上的解析可以并行解码一定高度的所有子树，从而导致对数运行时复杂性而不是线性。从建模的角度来看，自下而上的解析器在每个步骤中学习有意义的语义子程序的表示，而不是语义上呈现的部分树。我们将SMBOP应用于Spider，这是一个具有挑战性的零击语义解析基准，并表明SMBOP导致2.2倍的解码时间加速和$ \ sim $ \ sim $ 5倍的培训时间，与使用自动解析解码的语义解析器相比。 SMBOP在蜘蛛上获得了71.1的指示精度，建立了新的最先进的和69.5的精确匹配，可与自动回归的Rat-SQL+Grappa的69.6精确匹配相媲美。
The de-facto standard decoding method for semantic parsing in recent years has been to autoregressively decode the abstract syntax tree of the target program using a top-down depth-first traversal. In this work, we propose an alternative approach: a Semi-autoregressive Bottom-up Parser (SmBoP) that constructs at decoding step $t$ the top-$K$ sub-trees of height $\leq t$. Our parser enjoys several benefits compared to top-down autoregressive parsing. From an efficiency perspective, bottom-up parsing allows to decode all sub-trees of a certain height in parallel, leading to logarithmic runtime complexity rather than linear. From a modeling perspective, a bottom-up parser learns representations for meaningful semantic sub-programs at each step, rather than for semantically-vacuous partial trees. We apply SmBoP on Spider, a challenging zero-shot semantic parsing benchmark, and show that SmBoP leads to a 2.2x speed-up in decoding time and a $\sim$5x speed-up in training time, compared to a semantic parser that uses autoregressive decoding. SmBoP obtains 71.1 denotation accuracy on Spider, establishing a new state-of-the-art, and 69.5 exact match, comparable to the 69.6 exact match of the autoregressive RAT-SQL+GraPPa.
